Although LMNet [1]} can perform LiDAR-MOS only using a 2D segmentation network, the boundary-blurring effect is unavoidable due to the limited resolution of the feature maps and the dimensionality reduction of the range image representation. This leads to false positive predictions around object boundaries.
To tackle this issue, we propose a coarse-to-fine strategy.
Instead of only using the pixel-wise loss, we propose a point head to refine the segmentation results after the 2D convolutional network. This two-step coarse-to-fine strategy makes the supervision more effective and utilizes both pixel-wise and point-wise supervision.
To this end, we back-project/re-index the feature maps of the last layer to the original point locations. Then, we refine point-wise segmentation results by combining this with spatial information performing a relatively lightweight point cloud convolution operation.
Back-projection is performed with indices computed when the point cloud is projected to the 2D range image. As shown in Fig. REF , we back-project 2D feature maps \((C \times h \times w)\)  from ImageHead into point-wise features \((N \times C)\) . Then, we use points in Cartesian coordinates \((x,y,z)\)  and point-wise features to initially voxelize 3D sparse voxel. Finally, we use two branches of 3D sparse convolution and point cloud-based MLP to further refine the results using spatial geometry, and reduce the artifacts that occur around the object boundaries.
